The environment of virtual motion chambers must be designed to be arbitrarily controlled according to the purpose. Main environmental parameters include temperature, humidity, CO2 concentration, and oxygen concentration. In order to measure and control these parameters, environmental sensors should be installed in various parts of the chamber for real-time monitoring control.
Existing sensors usually consist of a transducer that maps physical quantities to electrical signals, an analog-to-digital converter (ADC), a microprocessor, and an output interface that maps them to standard analog or digital signals. To measure physical quantities, the systematic error component can be modified by calibrating the sensors.
For noise filtering, hardware methods (e.g., installing analog active and analog passive reference frequency filters at the input of ADC or installing discrete active reference frequency filters at the output) or program filters (average filters, moving average filters, Kalman filters and artificial neural network filters) can be used. The device active reference filter is bulky and expensive, and consumes a large amount of power, and the passive reference frequency filter is less efficient, so it is most reasonable to use a program filter.
Under operating conditions, the input of the sensor has continuous signals, which vary randomly with time, and the rate of change is limited by physical reasons. In this case, a distorted signal appears at the output of the filter. The dynamic distortion of effective signals by the average filter and the moving average filter depends on the maximum rate of change of input signals. Therefore, it is effective to use the average filter or moving average filter when the change rate of input signals is not large.
Han Chol Hak, a researcher at the Faculty of Automation Engineering, has proposed an improved additive-mean algorithm that suits the characteristics of objects to improve the accuracy of oxygen concentration measurements in environmental sensors, and demonstrated the superiority of its filtering effect through comparisons with other algorithms.
The improved average algorithm can be used effectively for oxygen concentration sensing in embedded systems as well as for processing signals with low measurement signal variation and high noise intensity.
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